no code implementations • 14 Dec 2023 • Yaela Gabay, Nir Shlezinger, Tirza Routtenberg, Yasaman Ghasempour, George C. Alexandropoulos, Yonina C. Eldar
THz communications are expected to play a profound role in future wireless systems.
no code implementations • 28 Nov 2023 • Itay Buchnik, Guy Sagi, Nimrod Leinwand, Yuval Loya, Nir Shlezinger, Tirza Routtenberg
Dynamic systems of graph signals are encountered in various applications, including social networks, power grids, and transportation.
1 code implementation • 6 Sep 2023 • Mengyuan Zhao, Guy Revach, Tirza Routtenberg, Nir Shlezinger
Achieving high-resolution Direction of Arrival (DoA) recovery typically requires high Signal to Noise Ratio (SNR) and a sufficiently large number of snapshots.
no code implementations • 22 Aug 2023 • Nadav Harel, Tirza Routtenberg
We show that the proposed performance bounds are more informative than the oracle Cram$\acute{\text{e}}$r-Rao Bound (CRB), where the third interpretation (selective inference) results in the lowest mean-squared-error (MSE) among the estimators.
no code implementations • 7 Aug 2023 • Morad Halihal, Tirza Routtenberg, H. Vincent Poor
In this paper, we investigate the problem of estimating a complex-valued Laplacian matrix with a focus on its application in the estimation of admittance matrices in power systems.
no code implementations • 31 May 2023 • Lital Dabush, Tirza Routtenberg
The proposed approaches are based on the representation of network data as the output of a graph filter with a given graph topology.
no code implementations • 21 Apr 2023 • Gal Morgenstern, Jip Kim, James Anderson, Gil Zussman, Tirza Routtenberg
We present the GFDI attack as the solution for a non-convex constrained optimization problem.
no code implementations • 17 Apr 2023 • Eyal Nitzan, Tirza Routtenberg, Joseph Tabrikian
The constrained Barankin-type bound (CBTB) is a nonlocal mean-squared-error (MSE) lower bound for constrained parameter estimation that does not require differentiability of the likelihood function.
Direction of Arrival Estimation Vocal Bursts Type Prediction
1 code implementation • 16 Apr 2023 • Itay Buchnik, Damiano Steger, Guy Revach, Ruud J. G. van Sloun, Tirza Routtenberg, Nir Shlezinger
In this work, we study tracking from high-dimensional measurements under complex settings using a hybrid model-based/data-driven approach.
no code implementations • 13 Feb 2023 • Yakov Medvedovsky, Eran Treister, Tirza Routtenberg
The Laplacian-constrained Gaussian Markov Random Field (LGMRF) is a common multivariate statistical model for learning a weighted sparse dependency graph from given data.
no code implementations • 23 Sep 2022 • Guy Sagi, Tirza Routtenberg
In this paper we propose two new estimators that are both based on the Gauss-Newton method: 1) the elementwise graph-frequency-domain MAP (eGFD-MAP) estimator; and 2) the graph signal processing MAP (GSP-MAP) estimator.
no code implementations • 22 Aug 2022 • Alon Amar, Tirza Routtenberg
For general Bayesian estimation of complex-valued vectors, it is known that the widely-linear minimum mean-squared-error (WLMMSE) estimator can achieve a lower mean-squared-error (MSE) than that of the linear minimum MSE (LMMSE) estimator.
no code implementations • 9 Jun 2022 • Nir Shlezinger, Tirza Routtenberg
While machine learning systems often lack the interpretability of traditional signal processing methods, we focus on a simple setting where one can interpret and compare the approaches in a tractable manner that is accessible and relevant to signal processing readers.
2 code implementations • 22 Sep 2021 • Julian P. Merkofer, Guy Revach, Nir Shlezinger, Tirza Routtenberg, Ruud J. G. van Sloun
A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice.
no code implementations • 4 Jun 2021 • Lital Dabush, Ariel Kroizer, Tirza Routtenberg
For simplicity, we start with analyzing the DC power flow (DC-PF) model and then extend our algorithms to the AC power flow (AC-PF) model.
no code implementations • 29 Mar 2021 • Ariel Kroizer, Tirza Routtenberg, Yonina C. Eldar
We show that the proposed sample-GSP estimators outperform the sample-LMMSE estimator for a limited training dataset and that the parametric GSP-LMMSE estimators are more robust to topology changes in the form of adding/removing vertices/edges.
no code implementations • 12 Feb 2021 • Shlomit Shaked, Tirza Routtenberg
We assume that the graph signals measured over the vertices of the network can be represented as white noise that has been filtered on the graph topology by a smooth graph filter.
no code implementations • 12 Jan 2021 • Shir Cohen, Tirza Routtenberg, Lang Tong
Finally, we demonstrate via numerical simulations that the proposed mmCCRB is a valid and informative lower bound on the mmMSE of state-of-the-art estimators for this problem: the CML, the Good-Turing, and Laplace estimators.
no code implementations • 17 Sep 2020 • Itai E. Berman, Tirza Routtenberg
We then solve the resource allocation optimization problem of the LGO model with the proposed tractable form of the MSE as an objective function and under a power constraint using a one-dimensional search.
no code implementations • 21 Aug 2020 • Eyal Levy, Tirza Routtenberg
In this paper, we consider the detection of a small change in the frequency of sinusoidal signals, which arises in various signal processing applications.
no code implementations • 5 May 2020 • Tirza Routtenberg
We develop sampling allocation policies that optimize sensor locations in a network for these problems based on the proposed graph CRB.
no code implementations • 19 Mar 2020 • Gal Morgenstern, Tirza Routtenberg
In this paper, we develop novel structural-constrained methods for the detection of unobservable FDI attacks, the identification of the attacked buses' locations, and PSSE under the presence of such attacks.
no code implementations • 15 Apr 2019 • Elad Meir, Tirza Routtenberg
Finally, we demonstrate in simulations that the proposed selective CRB is an informative lower bound on the performance of the maximum selected likelihood estimator for a general linear model with the generalized information criterion and for sparse vector estimation with one step thresholding.